The Next Web

Everything you need to know about model-free and model-based reinforcement learning

Reinforcement learning is one of the exciting branches of artificial intelligence. It plays an important role in game-playing AI systems, modern robots, chip-design systems, and other applications.
VentureBeat

Google researchers improve reinforcement learning by having their AI play Pong

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Deep reinforcement learning — an AI training technique that employs ...

Where Reinforcement Learning Plus Human Oversight Works Best

When RL is paired with human oversight, teams can shape how systems learn, correct course when context changes, and ensure ...
VentureBeat

DeepMind proposes novel way to train ‘safe’ reinforcement learning AI

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Reinforcement learning agents — or AI that’s progressively spurred toward ...
Semiconductor Engineering

DeepSeek: Improving Language Model Reasoning Capabilities Using Pure Reinforcement Learning

“We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT ...
Tech Xplore on MSN

Adaptive drafter model uses downtime to double LLM training speed

Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller ...
EurekAlert!

Exploiting large language model with reinforcement learning for generative job recommendations

With the rapid advancement of Large Language Models (LLMs), an increasing number of researchers are focusing on Generative Recommender Systems (GRSs). Unlike traditional recommendation systems that ...
inc42

What Is Reinforcement Learning? Here’s All You Need to Know

Reinforcement learning is a subfield of machine learning concerned with how an intelligent agent can learn through trial and error to make optimal decisions in its ...